Quantifying water balance dynamics and associated uncertainties in an irrigated catchment using open-source gridded datasets and hydrological modelling with improved process representation: case of Hindon River Basin, India

Irrigation can significantly influence the hydrological system of agriculturally productive catchments thus understanding the dynamics of the water balance is indispensable for sustainable (agricultural) water management. Accurate estimation of catchment water balance, using distributed hydrological modelling and earth observation data, requires thorough consideration of the associated uncertainties from different sources such as uncertainties in the model representation of system processes and the errors and uncertainties embedded in the remotely sensed data.

In this study, we estimate the spatial distribution and temporal dynamics of the water balance of the Hindon River Basin, a sub-basin of the Ganga River Basin in India, where intense irrigation has driven overexploitation of water resources, highly influencing the hydrological regime. For this estimation the open-source distributed hydrological model wflow_sbm is used and evaluated with open global datasets, demonstrating the applicability of this method in data scarce regions. We emphasize on representing relevant hydrological features of the Hindon Basin that are often neglected in hydrological model applications: irrigation, domestic and industrial water use, and (infiltrating) river-aquifer interaction. Our analysis includes the quantification and assessment of the uncertainties associated with the global datasets used and model representation of important processes. First, prior uncertainties of the input variables were analyzed by comparing the errors and correlations of the products from different sources. Second, the impact of different schematizations of irrigation application and subsurface flow (including river infiltration to aquifer) on the modelled water balance is assessed. Finally, model output is evaluated by comparing the estimates and uncertainties of modelled water balance components with estimates from various global datasets.

Our analysis reveals the impact of different model choices, highlighting the necessity of proper model representation of hydrological processes and uncertainty assessment to achieve a more reliable estimation of catchment water balance.